1. Technical Field
The present disclosure relates to a meaning generation method, a meaning generation apparatus that generate a meaning of an utterance sentence, and a storage medium.
2. Description of the Related Art
A meaning generation technique (an utterance intention generation technique) is a technique to convert utterances having the same meaning to meaning information (a meaning label). More specifically, using a training sentence including a set of expressions having the same meaning and meaning information, learning is performed in terms of features of a word or a phrase occurring frequently and contributing to meaning information (see, for example, Andrew M. Dai, Quoc V. Le, “Semi-supervised Sequence Learning”, NIPS 2015). It is also known, in a conventional technique, to learn conversion between character strings using a bilingual training sentence pair including a set of a Japanese sentence and an English sentence having the same meaning (see, for example, Ilya Sutskever, Oriol Vinyals, Quoc Le, “Sequence to Sequence Learning with Neural Networks”, NIPS 2014).
However, in these techniques, when an unimportant word having no direct contribution to an intention described in a sentence to be converted occurs at a high frequency, there is a possibility that the unimportant word is incorrectly recognized as an important word. Thus, to achieve a conversion to a correct meaning label, a further improvement is needed.